Name
Abstract | ||
|---|---|---|
For a linear system affected by real parametric uncertainty, this paper focuses on robust stability analysis via quadratic-in-the-state Lyapunov functions polynomially dependent on the parameters. The contribution is twofold. First, if n denotes the system order and m the number of parameters, it is shown that it is enough to seek a parameter- dependent Lyapunov function of given degree 2nm in the parameters. Second, it is shown that robust stability can be assessed by globally minimizing a multivariate scalar polynomial related with this Lyapunov matrix. A hierarchy of LMI relaxations is proposed to solve this problem numerically, yielding simultaneously upper and lower bounds on the global minimum with guarantee of asymptotic convergence. I. I NTRODUCTION |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CDC.2004.1428797 | Decision and Control, 2004. CDC. 43rd IEEE Conference |
Keywords | DocType | Volume |
Lyapunov matrix equations,linear matrix inequalities,linear systems,stability,uncertain systems,LMI relaxation,Lyapunov matrix,asymptotic convergence,linear systems,multivariate scalar polynomial,parameter-dependent Lyapunov function,quadratic-in-the-state Lyapunov function,real parametric uncertainty,robust stability analysis | Conference | 1 |
ISSN | ISBN | Citations |
0191-2216 | 0-7803-8682-5 | 33 |
PageRank | References | Authors |
3.13 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Henrion | 1 | 63 | 7.51 |
| Denis Arzelier | 2 | 279 | 26.58 |
| Peaucelle, Dimitri | 3 | 50 | 5.26 |
| Jean-Bernard Lasserre | 4 | 38 | 3.89 |